• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2081-2083
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• 2001

In this paper, without mathematical modeling dynamics, the plant parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.53-55
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• 2004

This paper deals with speed control of AC servo motor using a Adaptive fuzzy high gain observer. In this parer, the gain of the observer is properly set up using the fuzzy control and adaptive high gain observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. In order to verify the performance of the Adaptive fuzzy high gain observer which is proposed in this paper, it is compared estimate performance of High-gain Observer and Adaptive High Gain Observer with the computer simulation. Effectiveness of the proposed high gain observer is proved from the experiment to compare the case with a speed sensor to the case with Adaptive fuzzy high gain observer in the speed control of AC servo motor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.468-471
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• 2003

In this paper, a second order sliding mode control that combines with a fuzzy adaptation technique is presented for a nonlinear system with unknown dynamics. The chattering effect that is a representative disadvantage of the sliding mode control is avoided by using the second order sliding mode control instead of the first order sliding mode control. The proposed controller is composed of the equivalent control that is approximated by an online adaptation scheme and the hitting control that is used to constrain the states to maintain on the sub-sliding surface and used to guarantee the system robustness. Simulation results are presented to show the effectiveness of the proposed controller.

• 전기학회논문지
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• 제67권12호
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• pp.1699-1704
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• 2018

In this paper, proposed a method to improve of control characteristics for power facility using the adaptive sizing of fuzzy inference method. In the use of the controller based the fuzzy logic, a basic mamdani fuzzy controller is applied. However, when the maximum value and the minimum value have to taken, the fuzzy controller can not take a normal value because of formalized grouping form. In this paper, we combine the conventional methods with single valued sets to compensate for the disadvantage caused by the mamdani method control. Simulation results show that the proposed method has better overshoot and steady state arrival time than the conventional control method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.152-154
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• 2006

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for a vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for induction motor drive system

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.687-689
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• 1998

This paper represents fundamental developments in Fuzzy and Neural approaches. The Fuzzy Controller(FC) and plant are cascaded in Adaptive framework. Each of which produces its outputs. The adjustable parameters all pertain to the fuzzy controller is implemented as an Adaptive FC to adjust the environments of the plant. There is an error meaure block which is a difference between the actual state and desired state. We introduce error back propagation algorithm in neural method. To speed up convergence, we follow a steepest decent in the sense that each parameter set update leads to a smaller error measure and is learned by this methodology. Inverted pendulum is a typical testbed to measure the effectiveness of nonlinear control system. finally we simulated the adaptive fuzzy controller to be able to bring back to the upright position of the its angle and angular velocity.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권5호
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• pp.249-256
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• 2000

This paper deals with the control of the lateral motion of a mobile vehicle. A mobile vehicle using in this experiment is able to adapt many unmanned automatic driving system, for example, like a automated product transporting system. This vehicle is consist of the two servomotors. One is used to accelerate this vehicle and the another is used to change this lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral motion of the vehicle. Therefore, the main aim of this paper is investigate the possibility of applying adaptive fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. Fuzzy control rules are derived by modelling an expert's driving actions. Experiments are performed using a mobile vehicle with sensing units, a microprocessor and a host computer.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.